Adaptive active contour model integrating global and local image fitting energy

Zhu Xiaoshu; Sun Quansen; XIA Deshen

doi:10.11834/jig.20120909

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Adaptive active contour model integrating global and local image fitting energy
Vol. 17, Issue 9, Pages: 1109-1114(2012)
Published：2012
DOI： 10.11834/jig.20120909
稿件说明：

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DOI：

Zhu Xiaoshu, Sun Quansen, XIA Deshen. Adaptive active contour model integrating global and local image fitting energy[J]. Journal of Image and Graphics, 2012, 17(9): 1109-1114. DOI： 10.11834/jig.20120909.

摘要

提出一种新的基于全局图像信息和局部图像特征的活动轮廓分割模型。模型的总能量函数主要包括3项:全局能量项、局部能量项和自适应调节项。其中

全局能量项整合了图像的全局信息

局部能量项则考虑了图像的局部特征

而二者的权重会根据上下文内容自适应调整。由于在模型中充分利用了图像全局信息和局部特征

因而有效地提高了分割的精度。此外

加入了凸优化技术

以获取模型的全局最优解。最后

采用Split-Bregman方法进行快速求解

使得模型的分割效率大大提高。实验结果表明

该模型对初始化具有较好的鲁棒性

在分割精度上有了较大的提升

特别是分割速度比C-V模型快1.5倍到2倍。

Abstract

A new active contour model based on global and local image information is proposed for image segmentation. The energy functional for the proposed model consists of three terms

i.e.

the global intensity fitting term

the local intensity fitting term

and the adaptive parameter term. The global intensity fitting term incorporates global image information and the local intensity fitting term uses local contextual information. The weighting factor between the global and local intensity fitting term is adaptive by the image content.By incorporating the local and global image information into the proposed model

the images can be efficiently segmented. In addition

convex optimization is added to the new model to get the global minima. Finally

the Split-Bregman method can effectively improve the segmentation speed. Experimental results demonstrate that the proposed algorithm is robust to the choice of initialization values

can get the more accurate segmentation result

and especially is about 1.5 to 2 times faster than the C-V (Chan & Vese) model.